Add RNA Molecule
Add Molecule from API
Add Molecule from JSON
Add PDB File
Add MMCIF File
Edit Color Scheme
Share a Link of your View
About forna
RNA Secondary Structure Visualization Using a Force Directed Graph Layout
Overview
The forna
package provides a web interface for displaying RNA secondary
structures using the force-directed graph
layout provided by the
d3.js visualization library.
The front end makes use of Bootstrap and
Knockout.js for the user interfact while the back end
uses Flask to serve the static files and provide a
REST API. The RNA structure manipulation and graph construction is created
using the python forgi RNA
structure manipulation library and the provided forna.py
script. We heavily
depend on the python bindings of the ViennaRNA package.
Usage
Click on Add Molecule
to draw a new secondary structure. The input should
contain a sequence and a secondary structure in dot-bracket notation:
GCUUCAUAUAAUCCUAAUGAUAUGGUUUGGGAGUUUCUACCAAGAGCCUUAAACUCUUGAUUAUGAAGUG
((((((((((..((((((.........))))))......).((((((.......))))))..)))))))))
You can also use the FASTA format to add multiple molecule at once. If you do not enter a structure, it will calculate the MFE structure at 37°C for you.
In the FASTA header you can specify a name and optionally also the number of the first or last nucleotide:
>name|start=5|end=60
Zooming can be accomplished using the mouse wheel. Dragging the canvas leads to panning.
Colors
Position
Color the nucleotides according to their position in the molecule. Lower numbered nucleotides are closer to the 5' end and are colored green. Nucleotides in the middle are colored yellow whereas nucleotides near the 3' end are colored red.
Structure
Color the nucleotides according to the type of structure that they are in:
Green: Stems (canonical helices)
Red: Multiloops (junctions)
Yellow: Interior Loops
Blue: Hairpin loops
Orange: 5' and 3' unpaired region
Sequence
Color according to the nucleotide types:
Yellow: Adenine
Green: Cytosine
Blue: Uridine
Red: Guanine
Custom
Create a custom coloring scheme. The colors for each nucleotide or sets of nucleotide are specified in the text box that appears when the 'set' button is clicked. The format is simple. Numbers entered interpolated to colors and mapped to the respective nucleotides (the first number colors the first nucleotide, the second the second, etc...).
0.5 0.6 10 13.4This will color the first four nucleotides such that the first two are lightly colored and the last two are more darkly colored. The scale is interpolated between the lowest and highest value. We currently only use the white-blue color scale, but this will be expanded shortly. Coloring specific nucleotides is possible using the following format:
8:red 10:blue 11:greenThese directives will color the 8th nucleotide red, the 10th blue and the 11th green. Ranges of colors can also be entered:
1-10:green 15-30:red 13,46,101:blueThis will color nucleotides 1-10 green and nucleotides 15-30 red and 13,46, and 101 blue.
Colors can be specified for each molecule individually by preceding them with the molecule's id
>molecule1 1-10:green 15-30:red 13,46,101:blue >molecule2 0.98 .80 0.75
Color ranges and domains
The default color range is white to steelblue. Custom ranges can be specified by entering the following in the custom color screen.>molecule1 1-10:green 15-30:red 13,46,101:blue >molecule2 range=white:red 0.98 .80 0.75Color domains can be specified in a similar manner. The domain defines the values over which the colors are spread:
domain=0.5:1.0 0.98 .80 0.75
Pseudoknots
Feel free to enter structures with pseudoknots. Here's the format:>1GID AACCGG ((.[[.)).]]Use your imagination!
Editing the Structure
Links can artificially be added by holding down the shift key and dragging from one nucleotide to another. This will form a spring-loaded bond between two nucleotides. This is useful for drawing interacting pairs of structures. Bonds can be deleted by holding down the shift key and clicking on a bond.Centering the view
The view can be centered on molecules being displayed by pressing the 'c' key or by clicking on the crosshairs button in the lower left hand corner.Force
The force can be turned off by either hitting the space key or by flipping the switch in the lower left hand corner of the screen.Friction / Wobbliness
How 'wobbly' a molecule appears can be adjusted by the friction slider in the settings menu just to the right of the colors menu. Lowering the friction (paradoxically) makes the molecule less wobbly and increasing it makes it more so.PDB Files
Secondary structure can automatically be extracted from PDB files and displayed. Simply click on the dropdown arrow next to 'Add Molecule' and select 'Add PDB File'. This will import every RNA chain in the PDB file and display its secondary structure. Proteins will be added as nodes with a size proportional to their length. Interactions between separate protein and nucleotide chains are displayed as dashed lines. The secondary structure is extracted using MC-Annotate [1].Contact
Questions and/or comments can be sent to<EMAIL_ADDRESS>Acknowledgement
This work was funded by the Austrian DK RNA program FG748004, by the Austrian FWF, project "SFB F43 RNA regulation of the transcriptome," and the European Commission under the Environment Theme of the 7th Framework Program for Research and Technological Development (Grant agreement no: 323987).


+ty (Positivity) uses the Indico API to analyze your input and automatically determine its negative or positive rating.
I made Positivity with Maryam Archie during HackMIT 2015! Check it out on the Play Store.


fun hasModality(modality: ModalityType): Boolean
Check whether the current Id contains the input modality


Varadkar backs Verona Murphy as he plans to canvass in Wexford
Taoiseach Leo Varadkar is steadfastly backing his party’s by-election candidate Verona Murphy as he plans to head to Wexford to drum up support.
Controversial Fine Gael candidate Ms Murphy made comments to the Irish Times that so-called Islamic State is “a big part of the migrant population” in Ireland and services are needed to “alleviate that type of indoctrination”.
Ms Murphy also told RTE’s This Week programme that some asylum seekers coming to Ireland have to be “deprogrammed” as they “carry angst” and may have been “infiltrated by Isis”.
Ms Murphy has since apologised for her remarks and said she visited a reception centre for asylum seekers in Waterford “to gain a better understanding of asylum and refugee issues”.
Mr Varadkar has since expressed full confidence in Ms Murphy as Fine Gael’s by-election candidate.
“Her comments were wrong, ill-advised and misinformed,” he said last week.
“She is our candidate and I will be canvassing with her ahead of the by-election next Friday.”
Mr Varadkar said Ms Murphy has “strong views”.
Mr Varadkar, speaking on Monday while opening the new Mountview Shopping Centre in his constituency, said that he had “already had the conversation with” Ms Murphy and hoped to speak to her later in the day in Wexford.
Mr Varadkar would not take any more questions and media were prevented to ask anything further.
A number of Fine Gael ministers have been out on the by-election campaign trail with Ms Murphy, in a show of support despite the controversy over her comments on immigration and allegations of bullying while in her role as president of the Irish Road Haulage Association (IRHA), which she has stepped aside from for the duration of the campaign.
Justice Minister Charlie Flanagan and junior finance minister Michael D’Arcy have previously joined her for a canvass in Wexford Town.


When you click the button here, this simple app calls a flakey web service that takes longer and longer to respond. The app's circuit breaker is configured to timeout after 500ms and execute a fallback command. Every 20 seconds, the flakey service is reset and the pattern is repeated.
If more than half of the requests error, then it begins to fail fast, rejecting the network call outright and executing the fallback function.
The source code.
...


Hey! Thanks for visiting my page! My name is Jeff and I'm a software developer with a passion for all things tech! Since I was a kid, I was fortunate enough to get my hands on a computer and was instantly hooked on the possibilities. I was constantly fascinated by the constantly evolving user interface and experience on the web, so I chose to study in UI/UX design in college. During my time there I've gotten in touch with the world of programming, and have loved it since then. Sitting and typing aside I enjoy a large array of things that I cannot possibly handle in my lifetime. But right now I enjoy rock climbing to challenge myself to overcome my fear of heights and I really like the problem solving aspect of it!
Now that you know a little bit about me, let's try a fun quiz!


We are part of the
Perception
organization at Google AI, which tackles the hard problems of understanding images, sounds, music, and video.
If you have questions about the dataset, please contact<EMAIL_ADDRESS>The people who brought you this dataset:


Acknowledgements:
Portions of this product may utilize the following copyrighted material, the use of which is hereby acknowledged.
ASIHTTPRequest
The BSD License* Copyright © 2007-2011, All-Seeing Interactive
Reachability
The BSD License* Copyright (c) 2011-2013, Tony Million.
AAMFeedback
The BSD License* Copyright (c) 2011 Art & Mobile. All rights reserved.


When the amplitude of sleep activity is greater than 50, it can be determined as sleep agitation.
refers to unconscious body movement during sleep.
Turn around, turn more frequently at night, and sweat more.
Pay attention to your diet and drink a glass of drink before going to bed, such as milk or millet porridge. Make some preparations before going to bed, such as soaking your feet in hot water, Massage the foot's Yongquan and so on. Sleeping sideways can effectively reduce body movement. Improving the sleeping environment (indoor humidity, temperature) can also help.


Quantity of weather measured
| lib | phIoT |
|---|---|
| superkind | ph::Choice |
| WeatherPoint |
Weather observation point |
|---|
| cloudage | Marker |
The fraction of the sky obscured by clouds |
|---|---|---|
| dewPoint | Marker |
Dew point temperature |
| feelsLike | Marker |
Apparent temperature perceived when considering humidity, wind chill, and heat index |
| humidity | Marker |
Percent relative humidity |
| precipitation | Marker |
Amount of atmospheric water vapor fallen |
| pressure | Marker |
Barometric pressure; the mean atmospheric pressure at sea level |
| solarIrradiance | Marker |
The amount of energy arriving at the earth's surface |
| sunrise | Marker |
Historized trend of sunrise/sunsets as respective true/false transitions |
| temp | Marker |
Dry bulb air temperature |
| visibility | Marker |
Distance at which light can be clearly discerned measured |
| weatherCond | Marker |
Enumeration of the weather conditions (clear, cloudy, etc) |
| wetBulb | Marker |
Wet bulb air temperature |
| windDirection | Marker |
Compass direction from which wind originates |
| windSpeed | Marker |
Wind flow velocity |


Height (pixels)
Width (pixels)
Extremity
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Elevation
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Mountain/Valley
Valley
Mountain


Unless you have already designed the application to exclusively use AJAX in one form or another (REST, JSONRPC etc.) then it's pretty much a complete redesign. The reason is simple: "standard" web applications load up one page at a time from the server, with possibly some bits of javascript thrown in. Pyjamas applications are loaded once, as they are pure javascript.
From that point onwards, unless you want another large javascript "hit", the entire application should use AJAX to fetch any data, including HTML. If that sounds like it's a bit different from "normal" web applications, you'd be right. It's more like "Desktop" application development.
So - you can convert the Django (or other web app) one page at a time, to a format which separates out the data from its display. In other words: by the time you're done, there will be no templates left, only JSONRPC services (or other AJAX service / system). If that sounds like a lot of work, then you can temporarily embed the templates on-demand into iframes, either using a pyjamas.ui.HTMLPanel or by using a pyjamas.ui.Frame widget. If you've been told that Frames are a bad idea, you'd be right in the "normal" web development world, but completely wrong in this "Desktop-like" case. Thanks to the Pyjamas API, it's possible to set width and height and to react to Window Resize events, and thus to change the entire layout of the application depending on the user preferences, requirements, browser type and window size (as is done in the Pyjamas Mail example).


Class: Box::Account
- Inherits:
-
Object
- Object
- Box::Account
- Defined in:
- lib/box/account.rb
Overview
Represents an account on Box. In order to use the Box api, the user must first grant the application permission to use their account. This is done in the #authorize function. Once an account has been authorized, it can access all of the details and information stored on that account.
Instance Attribute Summary (collapse)
-
- (String) auth_token
readonly
The auth token if authorization was successful.
Instance Method Summary (collapse)
-
- (Boolean) authorize(details = nil) {|authorize_url| ... }
Authorize the account using the given auth token/ticket, or request permission from the user to let this application use their account.
-
- (Boolean) authorized?
Is the account authorized?.
-
- (Object) file(id)
Gets a file object by id.
-
- (Object) folder(id)
Gets a folder object by id.
-
- (Hash) info(refresh = false)
Return the account details.
-
- (Account) initialize(api)
constructor
Creates an account object using the given Box api key.
-
- (Boolean) logout
Log out of the account and invalidate the auth token.
-
- (Object) method_missing(sym, *args, &block)
Provides an easy way to access this account's info.
-
- (Boolean) register(email, password)
Register a new account on the Box website with the given details.
- - (Boolean) respond_to?(sym)
-
- (Folder) root
Get the root folder of the account.
-
- (String) ticket
Get the cached ticket or request a new one from the Box api.
Constructor Details
- (Account) initialize(api)
Creates an account object using the given Box api key. You can then #register a new account or #authorize an existing account.
20 21 22 23 24 25 |
# File 'lib/box/account.rb', line 20 def initialize(api) @api = case when api.class == Box::Api; api # use the api object as passed in else; Box::Api.new(api) # allows user to pass in a string end end |
Dynamic Method Handling
This class handles dynamic methods through the method_missing method
- (Object) method_missing(sym, *args, &block)
Provides an easy way to access this account's info.
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# File 'lib/box/account.rb', line 216 def method_missing(sym, *args, &block) super unless # TODO: Use symbols instead of strings str = sym.to_s return @info[str] if @info.key?(str) super end |
Instance Attribute Details
- (String) auth_token (readonly)
The auth token if authorization was successful.
13 14 15 |
# File 'lib/box/account.rb', line 13 def auth_token @auth_token end |
Instance Method Details
- (Boolean) authorize(details = nil) {|authorize_url| ... }
Authorize the account using the given auth token/ticket, or request permission from the user to let this application use their account.
An auth token can be reused from previous authorizations provided the user doesn't log out, and significantly speeds up the process. If the auth token if invalid or not provided, the account tries to log in normally and requires the user to log in and provide access for their account.
A ticket can be used for applications that do not block on the user, such as a website, where specifying a redirection url is not possible.
In order to maintain backwards compatibility, a ticket can only be specified in the hash syntax, while an auth token can be used in either the hash or string syntax.
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# File 'lib/box/account.rb', line 89 def (details = nil) # for backwards compatibility if details.is_a?(Hash) auth_token = details[:auth_token] ticket = details[:ticket] else auth_token = details ticket = nil end # use a saved auth token if it is given if auth_token return true if (auth_token) end # the auth token either failed or was not provided # we must try to authorize a ticket, and call the block if that fails if not (ticket) and block_given? # the supplied block should instruct the user to visit this url yield (ticket) # try authorizing once more (ticket) end # return our authorized status end |
- (Boolean) authorized?
Is the account authorized?
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# File 'lib/box/account.rb', line 202 def @info != nil end |
- (Object) file(id)
This file will not know its parent because of API short-comings. If you need the tree above this file, use root.find(:type => 'file', :id => id).first instead.
This function will return a file regardless of whether it actually exists. You will get exceptions if you try to access any info.
Gets a file object by id.
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# File 'lib/box/account.rb', line 197 def file(id) Box::File.new(@api, nil, :id => id) end |
- (Object) folder(id)
This folder will not know its parent because of API short-comings. If you need the tree above this folder, use root.find(:type => 'folder', :id => id).first instead.
This function will return a folder regardless of whether it actually exists. You will get exceptions if you try to access any info.
Gets a folder object by id.
181 182 183 |
# File 'lib/box/account.rb', line 181 def folder(id) Box::Folder.new(@api, nil, :id => id) end |
- (Hash) info(refresh = false)
Return the account details. A cached copy will be used if avaliable, and requested if it is not.
TODO: Add url to Box api documentation, and provide the current fields.
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# File 'lib/box/account.rb', line 146 def info(refresh = false) return @info if @info and not refresh begin cache_info(nil) # reset existing info info = @api.get_account_info['user'] cache_info(info) rescue Api::NotAuthorized, Api::InvalidInput nil end end |
- (Boolean) logout
The user will have to re-authorize if they wish to use this application, and the auth token will no longer work.
Log out of the account and invalidate the auth token.
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# File 'lib/box/account.rb', line 125 def logout begin @api.logout cache_token(nil) rescue Api::NotAuthorized # already logged out, or never logged in end true end |
- (Boolean) register(email, password)
Register a new account on the Box website with the given details.
36 37 38 39 40 41 42 43 |
# File 'lib/box/account.rb', line 36 def register(email, password) response = @api.register_new_user(email, password) cache_info(response['user']) # cache account_info, saving an extra API call (response['token']) true end |
- (Boolean) respond_to?(sym)
227 228 229 |
# File 'lib/box/account.rb', line 227 def respond_to?(sym) @info.key?(sym.to_s) or super end |
- (Folder) root
Get the root folder of the account. You can use this Folder object to access all sub items within the account. This folder is lazy loaded, and a network request will be made if/when the data is requested.
164 165 166 167 |
# File 'lib/box/account.rb', line 164 def root return @root if @root @root = folder(0) end |
- (String) ticket
Get the cached ticket or request a new one from the Box api.
208 209 210 |
# File 'lib/box/account.rb', line 208 def ticket @ticket ||= @api.get_ticket['ticket'] end |


The aim of this project is to visualize academic and professional relationships of committee members of the EuroVis 2021 conference.
I would like to extend my sincerest thanks to Professor Elisabeta Marai for her guidance throughout the course of the project. I would also like to give a shoutout to Waleed Hasan for his help with data gathering without whose work, this project would not have been possible.
This project was built using several Open Source components. The source code of these open source components along with license information can be found below. I acknowledge and am grateful to these developers for their contributions to the open source community.


Third Annual Western Montana Spring Training Series
Missoula Montana
* Saturday Mar.17 - Clinton Road Race 1 - 28 miles.
* Saturday Mar. 24 - Butler Creek Circuit Race 1 - 1 Hour
* Saturday Mar. 31 - Clinton Road Race II - 43 Miles.
* Saturday April 21 - Butler Creek Circuit Race II - 1 Hour
Start time for all races is 12:00 noon.
Registration for all races will be at the Big Sky Cyclery. (1110 South
Ave. West across from Fairgrounds) from 10:30 - 11:00.
Course Descriptions:
* Butler Creek Circuit - Hilly 1.7 mile circuit. Start is at
intersection of Expressway Ave. and Butler Creek Rd.
* Clinton Road Race - Mostly flat 8 mile circuit. Start is in
Clinton at intersection of frontage road and I-90 overpass. Finish
is @ three miles past the start on south frontage road.
Entry fee: $5.00 per race.
Categories are: A - USCF 1 - 4. * B - Women, USCF 5, Citizens and Juniors. Masters race their USCF category.
Races will be handicapped, with category B riders given a head start. All racers finish together and on the same lap. Lapped riders are encouraged to continue, but no contesting final main field sprint. Free lap for mechanical problems except for the Clinton RR. If weather is totally miserable (< 45 degrees with rain or snow - race will be postponed to Sunday).
Open to all cyclists, regardless of ability level. The purpose of this series is to gain experience and fitness with low key, fun races. Cyclists are encouraged to ride to the races from registration and continue after with a training ride through the lovely Montana countryside.
Races are sponsored by Five Valley Velo USCF Permit Pending.
Contact Fred Michini -<EMAIL_ADDRESS>or Phone-542-9114 for details.


Woman surprises mum when long lost friends appear in mirror as she has a haircut
by Laura AbernethyWhen Keanna Williams’ family moved from Georgia, back to Massachusetts after 13 years, it hit her mum hard.
It’s now been over two years since the move but Keanna felt her mum was still struggling – and she wanted to do what she could to cheer her up.
As a qualified hairdresser who posts as @thecurl.therapist, she told her mum she was going to style her hair – but it was much more than that.
Instead, she had contacted two of her mum’s oldest friends, one of whom she had only seen twice in 20 years, and arranged for them to appear in the mirror behind her as she had her hair cut.
Keanna caught the touching moment on camera as her mum broke down in tears and hugged both her friends and the moment quickly went viral with over 67,000 likes.
The 22-year-old tells Metro.co.uk: ‘The one she is hugging is my Godmother. She went to high school with my father and introduced him and mum. She was in my mom’s wedding and they travelled together.
‘The other one has been in her life for the same amount of time. I always considered them my mom’s extended sisters.’
But due to where they were all living and their busy lives, her mum hadn’t seen one of her friends in over two years and she had only seen the other once in 2011 and once around 2001.
After seeing how unhappy her mum was, Keanna wanted to do something to help. She reached out to one of the friends (her godmother) and asked to catch up.
.She explains: I told her everything that’s been happening since and before the move. I can tell she was really hurt to hear everything we’ve been going through, I heard her crying subtly.
‘I told her “she needs you guys back in her life, she needs her friends back.”
‘The next day she messaged me back and told me she and my mum’s other friend would be stopping by the salon I work at to see her.
‘She’s been begging me to do her hair so I figured kill 2 birds with one stone. I had them hiding behind a room divider directly behind that chair in the video and she had no idea.’
The women embraced each other as they came together once more.
And don’t worry, Keanna still cut her hair. A new style and old friends, what more could you need?


AWAKENINGS A film review by Frank Maloney Copyright 1991 Frank Maloney
Penny Marshall, the director of AWAKENINGS, has a way with actors, as anyone who has seen BIG will attest. The actors in this case are Robin Williams as a painfully shy doctor in a so-called chronic hospital in the Bronx, Robert De Niro as one of his patients whom he awakens from a decades-long "absence," Julie Kavner as Williams' nurse and supporter, an ensemble of wonderful character actors as other patients, and finally real-life patients in a real-life hospital.
The hospital is not ONE FLEW OVER THE CUCKOO'S NEST; sadism and power are not the problems when Williams shows up, albeit reluctantly, as the new doc-on-the-block. The problem is routine, lack of curiosity, acceptance of the received wisdom about warehousing the chronics, presumably untreatable victims of unknowable mental disorders. John Heard puts in an appearance as the stern, slightly cynical head doctor, whose job, it seems, is to rain on everyone's parade. (This is the same John Heard who played a similarly grim therapist in the Dyan Cannon movie I saw in previews earlier this last week.)
This is a movie that I think will stay with one for a long time because the story is so intriguing and the performances so strong. Williams discovers by accident that a group of patients who appear to totally out of communication with our world are maintaining slender threads of contact. He deduces a connection with an encephalitis epidemic in the 1920s and a possible cure in a new drug, L-DOPA (this being 1969). Williams experiments on De Niro, who awakens to find that 30 years of his life have disappeared. There is much of interest in the reactions of the patient and the family to this situation, the loving mother who doesn't really want her little boy to not need her, the man who finds that his family has disappeared in various way while he was "away," and so forth.
Williams plays against type and plays very well, with hardly any of his comic persona peeping through. He is never given a chance to extemporize, a la GOOD MORNING, VIETNAM, and turns an understated, disciplined performance. (Julie Kavner, his nurse, is her reliably wonderful self, just as we've seen in so many Woody Allen movies and elsewhere; but she is pretty much playing her same stock character, delightful as it is, in contrast to Williams.)
De Niro turns an amazingly detailed performance in a highly physical role of a man once total rigid like a statue, then gradually loosening up, learning to move again, then as the cure wears off, developing alarming tics and contortions of a body out of control. The only problem with De Niro's performance is that is De Niro, who may be too familiar a talent for us ever to forget that this is De Niro turning in another Oscar-nomination performance.
Some of the character actors that I would like mention en passant include Anne Meara, who was once, I believe, a stand-up comic, the late Dexter Gordon as the patient who will not or cannot speak except through his piano playing, and a particularly splendid Alice Drummond who has one of the best lines in the movie about knowing that the year is 1969 but needing it to be 1926 (the year she "went to sleep").
Some have complained that the movie suffers from an unnatural symmetry with De Niro trapped in his body and Williams trapped in his shyness and the two of them awakening to life, trying to help each other. I find this to be a thematic strength in the movie, the kind of quality that raises it above the disease-of-the-month club. It gives the movie meaning and resonance for all of us with message of appreciating life and having the courage to fight for it, a message not that different from the carpe diem theme of DEAD POETS SOCIETY, Robin Williams' second best movie to date.
I recommend AWAKENINGS. It is a strange and haunting story, excellently presented. You will very likely want to see more than once in fact.
-- Frank Richard Aloysius Jude Maloney .
The review above was posted to the
rec.arts.movies.reviews newsgroup (de.rec.film.kritiken for German reviews).
The Internet Movie Database accepts no responsibility for the contents of the
review and has no editorial control. Unless stated otherwise, the copyright
belongs to the author.
Please direct comments/criticisms of the review to relevant newsgroups.
Broken URLs inthe reviews are the responsibility of the author.
The formatting of the review is likely to differ from the original due
to ASCII to HTML conversion.
Related links: index of all rec.arts.movies.reviews reviews


SHOPPING LIST
Breads and Pastries
Cookies
Cake
Rolls
Baguette
Meat and Fish
Salmon
Tuna
Chicken
Turkey
Dairy
Ice Cream
Yogurt
Milk
Vegetables
Lettuce
Carrots
Red Peppers
Spinach
Asparagus
Fruits
Pinapple
Strawberries
Raspberries
Apples
Bananas


Start-TestServer
This Cmdlet will start an instance of the test server.
Start Server Parameters (Default)
This Cmdlet will allow you to start a local instance of the RPC test server for use in testing solidity contracts.
EXAMPLE 1
Basic use
C:\PS> Start-TestServer
This Cmdlet uses the previously defined values from the Initialize-Workspace Cmdlet (or the default values if none were provided) so no additional information is required to start the server.
EXAMPLE 2
Common Parameter use
C:\PS> Start-TestServer -Verbose
Using the Cmdlet with the -Verbose parameter will show any status messages as the server starts.
None
None
This command does not take input from the pipeline.
None
This command does not provide output for piping to other commands.
None


JavaScript and Ruby are very similar languages in some respects. However there are a few fundamental differences. Those we are not going to talk about today. Today we will be talking about one of the smaller differences, JavaScript Objects and Ruby Hashes.
Up to this point you are familiar with Ruby. In both Ruby and JavaScript the storage system Array takes the same name. However the closest correlation to a Hash in Ruby is called an Object in JavaScript. This can be confusing since in Ruby everything is an object. Let's start with an example of how you declare these. In Ruby a Hash is initialized like this:
hash = {
one: 1,
two: 2,
three: 3
}
There are of course a few different ways to declare a Hash in Ruby, I chose this one because it is the closest to an Object declaration in JavaScript. Take a look at this:
var object = {
one: 1,
two: 2,
three: 3
};
Except for the var they look identical don't they. Well, not quite.
In JavaScript, the Ruby key/value pair is called a name/value pair, which together are called the properties of the object. Although the value can be accessed in both languages using square bracket notation, in JavaScript you can also access the value by using this notation:
object.one
// => 1
which resembles a method call in Ruby. These two different ways to access an object brings up probably the major difference between Hashes and Objects. When you use the dot notation the word after the dot has to be an exact property name. But using square brackets notation is not the same in Ruby and JavaScript. In Ruby it is simply the default way to access a Hash an looks for what you wrote in the brackets. In JavaScript however, the language takes the expression inside the brackets and evaluates it then tries to look up the name using the result. This aspect creates some fundamental differences between Hashes and Objects.


Class google.ads.adwords.examples.v201101.GetCampaignAlerts
Extends
google.ads.adwords.examples.ExampleBase.
Defined in: GetCampaignAlerts.js.
| Constructor Attributes | Constructor Name and Description |
|---|---|
|
This code example illustrates how to retrieve campaign alerts for a user.
|
| Method Attributes | Method Name and Description |
|---|---|
|
run(user, callback)
Runs the code example.
|
- Methods borrowed from class google.ads.adwords.examples.ExampleBase:
- getTimeStamp, writeOutput
Class Detail
google.ads.adwords.examples.v201101.GetCampaignAlerts()
This code example illustrates how to retrieve campaign alerts for a user.
The alerts are restricted to a maximum of 10 entries.
Tags: AlertService.get
Method Detail
run(user, callback)
Runs the code example.
- Parameters:
- {google.ads.adwords.AdWordsUser} user
- AdWords user running the code example.
- {function} callback
- the callback method to be called once this example is complete.


--- layout: home author_profile: true ---
I am a researcher in the field of Artificial Intelligence (AI); currently a Postdoctoral Researcher at the Department of Advanced Computing Sciences of Maastricht University.
Outside of AI research, I am also interested in programming languages (pros and cons of different languages), optimising software (reading things like “What Every Programmer Should Know About Memory”), gaming, and esports.


I'm Diana, a collaborate and hardworking programmer, specialized in tools engineering, who is commited to sharpening my industry skills and improving the quality of my work. I strive to leverage my abilities to foster the creation of epic worlds, stories, and gaming experiences.
I love programming for the creative opportunities and challenges it reveals. Having worked just over a year in the professional world, however, means that I still have plenty to learn.
Through university and my game industry, I have built my skills in the following:
I thrive in team based environments. Recently, I have been working on tools that support the QA teams of several internal services and major franchises:
While studying Computer Science & Linguistics at the University of Illinois at Urbana-Champaign, I concurrently worked on several game projects:
Industry level projects I've worked on.
Some non-commercial game projects I've worked on.
See anything that peeks your interest? Contact me; I'd love to hear from you!


12 MozCon 2014 recaps for those who missed the event
17/Jul 2014
I always enjoy the opportunity to attend MozCon and to hear from some of the leading industry experts in SEO, social media, content marketing, anaytics, and brand and community building. The sessions are always jam packed with actionable tips, insights, and the after hours events provide ample opportunity to meet and network with fellow digital marketers
MozCon 2014 delivered in full with presentations from* Justin Cutroni, Phil Nottingham, Annie Cushing, Mike Ramsey, Richard Millington, Wil Reynolds* and much more (scroll to the end of the article for the full list). I hope you enjoy reading these recaps as much as I enjoyed writing them!
Welcome to MozCon 2014 with Rand Fishkin
Circle +Rand Fishkin on Google+
How to Use Social Science to Build Addictive Communities with Richard Millington
Circle +Richard Millington on Google+
Local Lessons from Small Town USA with Mike Ramsey
Circle +Mike Ramsey on Google+
Top 10 PR Tactics and Strategies of Successful Content and Link Building with Lexi Mills
Circle +Lexi Mills on Google+
YouTube: The Most Important Search Engine You Haven’t Optimized For with Phil Nottingham
Circle +Phil Nottingham on Google+
Supercharging Your Digital Analytics! with Justin Cutroni
Circle +Justin Cutroni on Google+
How to Never Run Out of Great Ideas with Pete Meyers
Circle +Pete Meyers on Google+
Google+ Game of Thrones: Claiming Your Kingdom for Brand Dominance with Mark Traphagen
Circle +Mark Traphagen on Google+
MozCon 2014: A Mozzy View with Sarah Bird and John Cook
Circle +Sarah Bird and +John Cook on Google+
Demystifying Data Visualization for Marketers with Annie Cushing
Circle +Annie Cushing on Google+
The Psychology of Persuasive Content for “Boring” Industries with Nathalie Nahai
Circle +Nathalie Nahai on Google+
Mad Science Experiments in SEO & Social Media with Rand Fishkin
Circle +Rand Fishkin on Google+
The other recaps
- Mobile SEO Geekout: Key Strategies and Concepts with +Cindy Krum
- Broken Brand Promises: The Disconnect Between Marketing and Customer Experience with +Kerry Bodine
- Improve Your SEO by Mastering These Core Principles with +Lindsay Wassell
- Architecting Great Experiments with +Kyle Rush
- Scaling Creativity: Making Content Marketing More Efficient with +Stacey Cavanagh
- Localizing your international content with +Zeph Snapp
- Taking about conversational search with +Justin Briggs
- Keep the Focus on the Doughnuts with +Marshall Simmonds
- Dare to Fail: How the Best Lessons Come in the Form of Defeat with +Jeremy Bloom
- You Are so Much More than an SEO with +Wil Reynolds
- Beyond SEO – Tactics for Delivering an Integrated Marketing with +Paddy Moogan
- Developing Your Own Great Interactive Content – What You’ll Need to Know with +Richard Baxter
- Prove Your Value with +Dana DiTomaso
All the speakers in one circle
If you want to follow all the speakers from MozCon 2014, you can circle these people :


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In inline hooking we overwrite the first 5 bytes of the funciton or API to redirect the flow of execution to our code. The 5 bytes can be JMP, PUSH, RET, or CALL.
Normal call:
Hooked call:
MessageBox starting bytes are overwritten by JMP to myHandler. We do our stuff and then transfer the control back to the original function (MessageBox).
To make a DLL we need the following things: - MessageBoxA API address i.e pointer
- Our function or code address i.e pointer
We can get MessageBoxA API address using GetProcAddress:- Get MessageBoxA address
- Get custom code or function address
- Overwrite bytes at #1 with JMP to #2
- Modify the parameter of original call
- Transfer control back to #1


The initial event in Lyme disease is often this skin rash: a reddened area, circular in shape, with more intense redness at the center. The rash itself will clear up without treatment, but the disease will spread to other areas of the body. You may initially have flu-like symptoms and/or a fever. As the disease progresses, you may also develop joint pain, weakness, and fatigue. Eventually the brain and heart will be damaged.
If you have this type of rash, contact your doctor immediately. Treating Lyme disease with antibiotics during the rash phase is very effective and will usually cure it. When the infection spreads to other parts of the body, treatment becomes more complicated. Chronic Lyme disease can be a very disabling illness.
Although Lyme disease is not very common, it does pose a threat, especially in the Midwest and along the Atlantic coast. The tick that transmits the disease is smaller than most, so the bite often goes unnoticed.
Rocky Mountain spotted fever germs gain entrance to the body through the bite of a carrier tick, and then spread throughout the body. Within fourteen days, you will develop the rash and a fever, often accompanied by weakness, chills, a headache, and a generalized flat rash. This rash has an unusual appearance and pattern that does not often occur in other acute illnesses associated with a fever. Since this disease can be fatal, immediate medical attention is necessary, even if the tick is not found. In 10 percent of cases, there is only a high fever and possibly a bad headache with no rash until it’s too late for you to be saved. If you develop an unexplained fever in the summertime, especially if you live in areas where cases of Rocky Mountain spotted fever are commonly reported, seek medical attention. In the United States, these include North Carolina and Oklahoma.
Also, ticks need time to spread disease, so check your body for ticks immediately after you return from the woods.  Make certain you remove the entire tick by placing the tweezers at the base of the head (where the neck would be) and pulling gently.
Hives are most commonly the result of a generalized allergic reaction to foods, pollen, or medication. Exposure to heat and cold, sunlight, stress, and infections can also influence the development of hives. The recurring nature of hives is the most frustrating feature. Once they appear, they may improve, only to pop out again a few days later, without any known re-exposure to the precipitating factor.
Certain blood-borne and other cancers can cause hives, such as leukemia and cancer of the lymph system. Although this rarely occurs, it is advisable to check with your physician, who may do more extensive testing if he or she is suspicious that a serious condition is causing the hives (also see Tips 154, 169, and 170).


RDF Binary using Apache Thrift
RDF Binary using Apache Thrift (“RDF Thrift”) is a binary format for RDF and RDF-related data.
The W3C standard RDF syntaxes are text or XML based. These incur costs in parsing; the most human-readable formats also incur high costs to write, and have limited scalability due to the need to analyse the data for pretty printing rather than simply stream to output.
N-Triples or N-Quads are often used for datastore dump formats and for publishing large datasets because they have been found to be the best formats to read and write for this usages. Yet these are still text formats.
SPARQL result sets have a number of related syntaxes, based on using JSON, using XML, or using TSV or CSV. Again, these are text-based formats.
Binary formats are faster to process - they do not incur the parsing costs of text-base formats. “RDF Thrift” defines basic encoding for RDF terms then builds data formats for RDF graphs, RDF datasets and for SPARQL result sets. This gives a basis for high-performance linked data systems.
Apache Thrift provides a efficient, wide-used binary encoding layer with a large number of language bindings. (Thrift also provides a cross-language, service interaction model on top of this encoding layer - this is not used here.)
-
Format description: RDF Binary encoding using Thrift.
-
Known implementations:
- Apache Jena: Jena API to RDF Trift.
Document license: Apache License .


3 **bluez_tools** is a set of C99 modules aiming to provide an user-friendly upper layer to the well-known Bluetooth library [BlueZ](http://www.bluez.org/). Each module targets a different aspect: from the Bluetooth adapter (dongle) management to the creation and management of fully functionnal L2CAP-based servers.
5 This project is the conclusion of a school project done in may 2014. The goal of this project was to provide a way to implement an indoor positionning system based on Bluetooth LE (Low Energy). In order to do so I created an API for my team to use BlueZ functionnalities with more ease. Since the BlueZ API is not very well documented itself (its documentation lies in the [official Bluetooth core specification](https://www.bluetooth.com/specifications/adopted-specifications)) it was essential to provide a more easy-to-use layer. The bluez_tools modules are the latest representation of this API.
7 The end-product of the previous school project is given as an example of the bluez_tools use. The main setup was as follow: three Raspberry Pis with Bluetooth dongles attached to them where used as servers receiving RSSI data through L2CAP protocole over Bluetooth from a Bluetooth beacon attached to a person in the room. The computed RSSI data, having been previously calibrated, are then computed to locate the relative coordinates of the beacon inside the room. This school project was created by the ENSIMAG FabLab team : BERTAULD Thomas, BIANCHERI Cédric, BRELOT Lucie and FONT Ludovic.
11 * User-friendly API: the provided modules are a wrapper on the BlueZ library, taking care for you of the error handling and low-level return values, allowing you to concentrate on coding your application.
12 * Safe: internal mechanicals are in place to ensure as little error cases as possible. For instance, the Bluetooth adapter's HCI is seen as a state machine, allowing you to recover from a potential lockout (say for instance if the dongle stays in the "scan" mode).
13 * Both low-level and high-level management: functions exist for both low-level management such as sockets management as well as for high-level management such as the creation of a Bluetooth server using the L2CAP protocole.
14 * Modularity: each module can be used separately (though all rely on the "bt" one) and it's easy to reuse the existing ones to create even higher layers or to enhance the core functionnalities.
16 **Current limitations**:
18 * Non-optimal performances: the use of heavy-load functions such as strings concatenation or snprintf to compute RSSI inquiries and the mechanisms in place to ensure the functionnalities safety can affect the performances of the application.
19 * The creation of a L2CAP server currently leads to the creation of a thread for each connected client.
23 * BlueZ v5.2 or higher
27 ![bluez_tools modules overview](doc/modules_scheme.png)
31 ### Compiling and installing the modules
33 1. You first need to edit the **headers** to ensure that the modules
34 are configured to suit your needs (especially regarding the timeout set
35 to access the controller or to send an inquiry).
37 2. Then simply compile the modules using the Makefile provided in the **src** directory. You can specify whether or not to use a cross-compiling toolchain by setting the CROSS_COMPILE flag. You can also decide if you rather want the resulting
38 libraries to be static (.a) or shared (.so) by setting the LIBTYPE flag. The default behavior is to produce a shared library.
49 If everything went well, the library should have been produced in the **lib**
52 3. You can now install the library and the required headers. The **INSTALL_LIB_PATH** and **INSTALL_H_PATH** flags can be set to provide a custom installation path. The default values are /usr/local/lib and /usr/local/include
60 make install INSTALL_LIB_PATH=/home/my/path
63 You can also re-compile the provided documentation by using the Makefile inside the **doc** directory.
65 #### Compiling your code
67 Now that we have everything ready, we just need to compile our application.
69 First of all, make sure that the previously generated library can be
70 found by the compiler/linker. Assuming that you're using gcc, you just need to
71 specify the -L option :
74 gcc -L path/to/libraries/
77 You will then have to build the host program using the **lbluez_tools** library:
80 gcc -L path/to/libraries/ main.c -lbarelog_logger
83 Of course, this need to be adapted in case you use another compiler.
89 Some modules provide a unit test case that can be performed by enabling the right pre-processor macro. For instance, you can build a test-case for the hci_socket module by defining the **HCI_SOCKET_TEST** macro.
91 A single test for RSSI measurement is also provided under the **tests** directory.
95 As previously said, the demo is to run on three L2CAP servers in charge of receiving RSSI values, a beacon in charge of sending those values and a L2CAP client computing all the RSSI values coming from the three servers, thus creating a **triangulation** system. The setup we used was as follow:
96 * A TI Bluetooth LE beacon was the target to track.
97 * Three Raspberry Pis equiped with Bluetooth dongles were used as servers, receiving RSSI measures from the beacon.
98 * A laptop was used as the computation system (client), receiving data from the three servers.
99 * The data were then sent to a visualization application which is not part of this demo.
101 To compile the demo simply use the provided makefile:
107 The first command will build the client's side of the application and the second one the servers side.


|
App Engine Python SDK
v1.6.9 rev.445
The Python runtime is available as an experimental Preview feature.
|
Public Member Functions | |
| def | __init__ |
| def | __str__ |
| def | __repr__ |
| def | __cmp__ |
| def | __hash__ |
| def | ToXml |
Key used to identify a blob in Blobstore. This object wraps a string that gets used internally by the Blobstore API to identify application blobs. The BlobKey corresponds to the entity name of the underlying BlobReference entity. This class is exposed in the API in both google.appengine.ext.db and google.appengine.ext.blobstore.
| def google.appengine.api.datastore_types.BlobKey.__init__ | ( | self, | |
| blob_key | |||
| ) |
Constructor. Used to convert a string to a BlobKey. Normally used internally by Blobstore API. Args: blob_key: Key name of BlobReference that this key belongs to.
| def google.appengine.api.datastore_types.BlobKey.__repr__ | ( | self | ) |
Returns an eval()able string representation of this key. Returns a Python string of the form 'datastore_types.BlobKey(...)' that can be used to recreate this key. Returns: string
| def google.appengine.api.datastore_types.BlobKey.__str__ | ( | self | ) |
Convert to string.


Facelook
Welcome to Facelook!
A simple Facebook clone.
Add your post from here
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Hello World!
This is Jeffrey10th's project webpage. Explore his projects:
Jeffrey10th/cpp
Jeffrey10th/testing
Contribute:
Contribute to website.
This webpage is licensed under the
Apache License, Version 2
. Please read and comply with the license.


{% extends 'dashboard/base.html' %} {% block dashboard_body %} {{ _('Account Settings') }}
{{ _('To change your avatar, please visit {startlink}Gravatar{endlink}.').format(startlink='', endlink='')|safe }}
{{ _('Passwords must be at least eight characters') }}
{% trans %}To change your email, enter your new email address. We will send you a confirmation message containing a link that will finalize the process.{% endtrans %}


|
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java.lang.Object | +--org.apache.xerces.framework.XMLAttrList
An instance of this class is used to represent the set of attributes for an element that are either directly specified or provided through a default value in the grammar for the document. XMLAttrList carries the attributes associated with an element from the scanner up to the application level (via the SAX AtributeList). Because all the attributes are bundled up together before being presented to the application, we don't have a way to build up an attribute value from pieces, most notably entity references.
There is typically one instance of this class for each instance of a parser. The parser may either use this object to hold the attributes of a single element, calling releaseAttrList() before each new element, or it may use this object to hold the attributes of all of the elements in the document.
To start saving a new set of attributes, the startAttrList() method is called, returning a handle for the attribute list. All addAttr() calls will be added to the set until a call is made to endAttrList(). A handle of -1 is used to indicate that there are no attributes in the set.
When an attribute is added to the set, the type of the attribute and an indicator of whether it was specified explicitly or through a default is provided.
The attributes in the set may be accessed either through the getFirstAttr()
and getNextAttr() iteration interface, or the getAttributeList() method
may be used to access the attribute list through the SAX AttributeList
interface.
| Constructor Summary | |
XMLAttrList(StringPool stringPool)
Constructor |
| Method Summary | |
int |
addAttr(int attrName,
int attValue,
int attType,
boolean specified,
boolean search)
|
int |
addAttr(QName attribute,
int attValue,
int attType,
boolean specified,
boolean search)
Add an attribute to the current set. |
int[] |
endAttrList()
Terminate the current set of attributes. |
AttributeList |
getAttributeList(int attrListHandle)
Setup this instance to respond as an AttributeList implementation. |
int |
getAttrLocalpart(int attrIndex)
Return the localpart of the attribute. |
int |
getAttrName(int attrIndex)
Get the name of the attribute |
int |
getAttrPrefix(int attrIndex)
Get the prefix of the attribute. |
int |
getAttrURI(int attrIndex)
Return the uri of the attribute. |
int |
getAttType(int attrIndex)
Get the type of the attribute |
int |
getAttValue(int attrIndex)
Get the value of the attribute |
int |
getFirstAttr(int attrListHandle)
Get the first attribute in the attribute list. |
int |
getLength()
Return the number of attributes in this list. |
java.lang.String |
getLocalpart(int i)
Return the local part of an attribute in this list (by position). |
java.lang.String |
getName(int i)
Return the name of an attribute in this list (by position). |
int |
getNextAttr(int attrIndex)
Get the next attribute in the attribute list. |
java.lang.String |
getPrefix(int i)
Return the prefix of an attribute in this list (by position). |
java.lang.String |
getType(int i)
Return the type of an attribute in the list (by position). |
java.lang.String |
getType(java.lang.String name)
Return the type of an attribute in the list (by name). |
java.lang.String |
getURI(int i)
Returns the URI of an attribute in this list (by position). |
java.lang.String |
getValue(int i)
Return the value of an attribute in the list (by position). |
java.lang.String |
getValue(java.lang.String name)
Return the value of an attribute in the list (by name). |
boolean |
isSpecified(int attrIndex)
Was the attribute explicitly supplied or was it provided through a default? |
void |
releaseAttrList(int attrListHandle)
Make the resources of the current attribute list available for reuse. |
void |
reset(StringPool stringPool)
Reset this instance to an "empty" state. |
void |
setAttrURI(int attrIndex,
int uri)
Sets the uri of the attribute. |
void |
setAttType(int attrIndex,
int attTypeIndex)
Sets the type of the attribute. |
void |
setAttValue(int attrIndex,
int attrValue)
Sets the value of the attribute. |
int |
startAttrList()
Start a new set of attributes. |
| Methods inherited from class java.lang.Object |
clone,
equals,
finalize,
getClass,
hashCode,
notify,
notifyAll,
toString,
wait,
wait,
wait |
| Constructor Detail |
public XMLAttrList(StringPool stringPool)
stringPool
- The string pool instance to use.| Method Detail |
public void reset(StringPool stringPool)
stringPool
- The string pool instance to use.public int addAttr(int attrName, int attValue, int attType, boolean specified, boolean search) throws java.lang.Exception
public int addAttr(QName attribute, int attValue, int attType, boolean specified, boolean search) throws java.lang.Exception
attrName
- The name of the attribute, an index in the string pool.attValue
- The value of the attribute, an index in the string pool.attType
- The type of the attribute, an index in the string pool.specified
- true
if the attribute is specified directly; otherwise
false
is the attribute is provided through a default.search
- true
if the list should be searched for a duplicate.search
was true
and attrName
was already present.public int startAttrList()
public int[] endAttrList()
public int getAttrPrefix(int attrIndex)
public int getAttrLocalpart(int attrIndex)
public int getAttrName(int attrIndex)
attrIndex
- The index of the attribute.public void setAttrURI(int attrIndex, int uri)
public int getAttrURI(int attrIndex)
public int getAttValue(int attrIndex)
attrIndex
- The index of the attribute.public void setAttValue(int attrIndex, int attrValue)
public void setAttType(int attrIndex, int attTypeIndex)
public int getAttType(int attrIndex)
attrIndex
- The index of the attribute.public boolean isSpecified(int attrIndex)
attrIndex
- The index of the attribute.true
if the attribute was specified directly; otherwise
false
is the attribute was provided through a default.public void releaseAttrList(int attrListHandle)
The
- attribute list handle.public int getFirstAttr(int attrListHandle)
attrListHandle
- The attribute list handle.public int getNextAttr(int attrIndex)
attrIndex
- The attribute index.attrIndex
in
the same attribute list or -1 if there is no next index.public AttributeList getAttributeList(int attrListHandle)
AttributeList
implementation.AttributeList
.public int getLength()
The SAX parser may provide attributes in any arbitrary order, regardless of the order in which they were declared or specified. The number of attributes may be zero.
public java.lang.String getPrefix(int i)
public java.lang.String getLocalpart(int i)
public java.lang.String getName(int i)
The names must be unique: the SAX parser shall not include the same attribute twice. Attributes without values (those declared #IMPLIED without a value specified in the start tag) will be omitted from the list.
If the attribute name has a namespace prefix, the prefix will still be attached.
i
- The index of the attribute in the list (starting at 0).getLength()
public java.lang.String getURI(int i)
public java.lang.String getType(int i)
The attribute type is one of the strings "CDATA", "ID", "IDREF", "IDREFS", "NMTOKEN", "NMTOKENS", "ENTITY", "ENTITIES", or "NOTATION" (always in upper case).
If the parser has not read a declaration for the attribute, or if the parser does not report attribute types, then it must return the value "CDATA" as stated in the XML 1.0 Recommentation (clause 3.3.3, "Attribute-Value Normalization").
For an enumerated attribute that is not a notation, the parser will report the type as "NMTOKEN".
i
- The index of the attribute in the list (starting at 0).getLength()
,
getType(java.lang.String)
public java.lang.String getValue(int i)
If the attribute value is a list of tokens (IDREFS, ENTITIES, or NMTOKENS), the tokens will be concatenated into a single string separated by whitespace.
i
- The index of the attribute in the list (starting at 0).getLength()
,
getValue(java.lang.String)
public java.lang.String getType(java.lang.String name)
The return value is the same as the return value for getType(int).
If the attribute name has a namespace prefix in the document, the application must include the prefix here.
name
- The name of the attribute.getType(int)
public java.lang.String getValue(java.lang.String name)
The return value is the same as the return value for getValue(int).
If the attribute name has a namespace prefix in the document, the application must include the prefix here.
i
- The index of the attribute in the list.getValue(int)
|
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The Causata package provides utilities for extracting data from the Causata application, training binary classification models, and exporting models as PMML for scoring.
| Version: | 4.2-0 |
| Depends: | R (≥ 2.15.1) |
| Imports: | XML, R.utils, rjson, RMySQL, RCurl, stringr, yaml, boot, foreach, data.table, glmnet, ggplot2 |
| Suggests: | doMC, testthat, pROC, knitr |
| Published: | 2016-12-05 |
| Author: | Justin Hemann, David Barker, Suzanne Weller, Jason McFall |
| Maintainer: | Justin Hemann <justinh at causata.com> |
| License: | GPL-2 | GPL-3 [expanded from: GPL] |
| URL: | www.causata.com |
| NeedsCompilation: | no |
| Materials: | README NEWS |
| CRAN checks: | Causata results |
| Reference manual: | Causata.pdf |
| Vignettes: |
Model training and export |
| Package source: | Causata_4.2-0.tar.gz |
| Windows binaries: | r-devel: Causata_4.2-0.zip, r-release: Causata_4.2-0.zip, r-oldrel: Causata_4.2-0.zip |
| OS X binaries: | r-release: Causata_4.2-0.tgz, r-oldrel: Causata_4.2-0.tgz |
| Old sources: | Causata archive |
Please use the canonical form https://CRAN.R-project.org/package=Causata to link to this page.


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Integrated trading strategy¶
In this tutorial, you will learn:
How to make use of different features to write a strategy
How to use machine learning models to predict trading signals
Putting it all together¶
In this part we’ll look into how to put everything together in order to build a strategy that
incorporates different features (technical indicator, macroeconomic trends and sentiment indicator) so as to
comprehensively examine the market as a whole.
Simple approach¶
We’ll first look into simplistic, rule-based methods to make use of the features in an
algorithmic trading pipeline.
Baseline model¶
We have created the Baseline model example to demonstrate how we could generate signals using a technical
analysis strategy, then filter it by macroeconomic data and sentiment labels. The implementation of the
baseline model could be found in
integrated-strategy/baseline.py
.The workflow of the baseline model is as follows:
Generate the signals dataframe using a technical analysis strategy (e.g. MACD)
Pass the signals to the macroeconomic filter
Pass the signals to the sentiment filter
Backtest with the filtered signals dataframe
As usual, we first select the ticker and time range to run the strategy:
# load price data
df_whole = pd.read_csv('../../database/microeconomic_data/hkex_ticks_day/hkex_0001.csv', header=0, index_col='Date', parse_dates=True)
ticker = "0005.HK"
# select time range (for trading)
start_date = pd.Timestamp('2017-01-01')
end_date = pd.Timestamp('2021-01-01')
df = df_whole.loc[start_date:end_date]
Note that we’ll also need this
filered_df
additionally to calculate the stock price’s
sensitivity to economic indicators.# get filtered df for macro analysis
filtered_df = df_whole.loc[:end_date]
In the example, we choose to apply the MACD crossover strategy.
# apply MACD crossover strategy
macd_cross = macdCrossover(df)
macd_fig = macd_cross.plot_MACD()
plt.close() # hide figure
# get signals dataframe
signals = macd_cross.gen_signals()
print(signals.head())
signal_fig = macd_cross.plot_signals(signals)
plt.close() # hide figure
After obtaining the signals dataframe by running the technical analysis strategy, we’ll pass it to
the macroeconomic filters and sentiment filter to eliminate signals that are contradicatory with the
economic indicator or sentiment labels.
In this code snippet, we first apply the macroeconomic filter:
# get ticker's sensitivity to macro data
s_gdp, s_unemploy, s_property = GetSensitivity(filtered_df)
# append signals with macro data
signals = GetMacrodata(signals)
# calculate adjusting factor
signals['macro_factor'] = s_gdp * signals['GDP'] + s_unemploy * signals['Unemployment rate'] + s_property * signals['Property price']
signals['signal'] = signals['signal'] + signals['macro_factor']
# round off signals['signal'] to the nearest integer
signals['signal'] = signals['signal'].round(0)
We then apply the sentiment filter:
filtered_signals = SentimentFilter(ticker, signals)
With this filtered signals dataframe, we could pass it directly to the backtesting function
in order to evaluate the portfolio performance.
portfolio, backtest_fig = Backtest(ticker, filtered_signals, df)
plt.close() # hide figure
# print stats
print("Final total value: {value:.4f} ".format(value=portfolio['total'][-1]))
print("Total return: {value:.4f}%".format(value=(((portfolio['total'][-1] - portfolio['total'][0])/portfolio['total'][-1]) * 100))) # for analysis
print("No. of trade: {value}".format(value=len(signals[signals.positions == 1])))
We could also make use of the evaluation metric functions:
# Evaluate strategy
# 1. Portfolio return
returns_fig = PortfolioReturn(portfolio)
returns_fig.suptitle('Baseline - Portfolio return')
#returns_fig.savefig('./figures/baseline_portfolo-return')
plt.show()
# 2. Sharpe ratio
sharpe_ratio = SharpeRatio(portfolio)
print("Sharpe ratio: {ratio:.4f} ".format(ratio = sharpe_ratio))
# 3. Maximum drawdown
maxDrawdown_fig, max_daily_drawdown, daily_drawdown = MaxDrawdown(df)
maxDrawdown_fig.suptitle('Baseline - Maximum drawdown', fontsize=14)
#maxDrawdown_fig.savefig('./figures/baseline_maximum-drawdown')
plt.show()
# 4. Compound Annual Growth Rate
cagr = CAGR(portfolio)
print("CAGR: {cagr:.4f} ".format(cagr = cagr))
Machine learning approach¶
Moving on, we’ll look into more advanced methods that pass the data features into a machine learning model
in order to make sequential predictions.
Recurrent Neural Networks¶
A recurrent neural network (RNN) is a type of artificial neural network designed for sequential data or time series data.
As opposed to traditional feedforward neural networks, they are networks with loops in them which allow information to persist.
It has a lot of applications, ranging from language modelling to speech recognition. You can read more about the it in Andrej
Karpathy’s blog post - The Unreasonable Effectiveness of Recurrent Neural Networks.
However, one problem with RNNs is that they have a hard time in capturing long-term dependencies. When making a prediction at the
current time step, RNNs would weigh more recent historical information to be more important. But sometimes contextual information could
lie in the far past. This is When LSTM comes into play.
Long Short Term Memory networks (LSTMs) is an improved version of RNN that is specifically designed to avoid the long-term dependency problem.
Their default behaviour is to remember information for long periods of time.
If you want to know more about the mechanism and details of LSTMs, you could read this great blog post -
Understanding LSTM Networks.
Single-feature LSTM model¶
The implememtation of the single-feature LSTM model could be found in
integrated-strategy/LSTM-train_price-only.py
.We’ll make use of the PyTorch library to build the LSTM model. You could
install the library from here: https://pytorch.org/.
We first load the data for training and testing respectively.
data_dir = "../../database/microeconomic_data/hkex_ticks_day/"
# select date range
dates = pd.date_range('2010-01-02','2016-12-31',freq='B')
test_dates = pd.date_range('2017-01-03','2020-09-30',freq='B')
# select ticker
symbol = "0001"
# load data
df = read_data(data_dir, symbol, dates)
df_test = read_data(data_dir, symbol, test_dates)
The
MinMaxScaler
function from the sklearn.preprocessing
is used to
normalise the input features, i.e. they will be transformed into the range [-1,1] in the
following code snippet.scaler = MinMaxScaler(feature_range=(-1, 1))
df['Close'] = scaler.fit_transform(df['Close'].values.reshape(-1,1))
df_test['Close'] = scaler.fit_transform(df_test['Close'].values.reshape(-1,1))
look_back = 60 # choose sequence length
We can check the shapes of the train and test data:
x_train, y_train, x_test, y_test = load_data(df, look_back)
print('x_train.shape = ',x_train.shape)
print('y_train.shape = ',y_train.shape)
print('x_test.shape = ',x_test.shape)
print('y_test.shape = ',y_test.shape)
And then make the traing and testing sets in torch:
# make training and test sets in torch
x_train = torch.from_numpy(x_train).type(torch.Tensor)
x_test = torch.from_numpy(x_test).type(torch.Tensor)
y_train = torch.from_numpy(y_train).type(torch.Tensor)
y_test = torch.from_numpy(y_test).type(torch.Tensor)
Moving on, let’s set the hyperparameters.
# Hyperparameters
n_steps = look_back - 1
batch_size = 32
num_epochs = 100
input_dim = 1
hidden_dim = 32
num_layers = 2
output_dim = 1
torch.manual_seed(1) # set seed
We’ll use mean squared error (MSE) as the loss function, and use Adam as the optimiser with a learning rate
of 0.01.
train = torch.utils.data.TensorDataset(x_train,y_train)
test = torch.utils.data.TensorDataset(x_test,y_test)
train_loader = torch.utils.data.DataLoader(dataset=train,
batch_size=batch_size,
shuffle=False)
test_loader = torch.utils.data.DataLoader(dataset=test,
batch_size=batch_size,
shuffle=False)
model = LSTM(input_dim=input_dim, hidden_dim=hidden_dim, output_dim=output_dim, num_layers=num_layers)
loss_fn = torch.nn.MSELoss()
optimiser = torch.optim.Adam(model.parameters(), lr=0.01)
We’ll write the training loop now:
# Initialise a list to store the losses
hist = np.zeros(num_epochs)
# Number of steps to unroll
seq_dim = look_back - 1
# Train model
for t in range(num_epochs):
# Forward pass
y_train_pred = model(x_train)
loss = loss_fn(y_train_pred, y_train)
if t % 10 == 0 and t !=0:
print("Epoch ", t, "MSE: ", loss.item())
hist[t] = loss.item()
# Zero out gradient, else they will accumulate between epochs
optimiser.zero_grad()
# Backward pass
loss.backward()
# Update parameters
optimiser.step()
We could now make predictions on the test set to get the MSE:
# Make predictions
y_test_pred = model(x_test)
# Invert predictions
y_train_pred = scaler.inverse_transform(y_train_pred.detach().numpy())
y_train = scaler.inverse_transform(y_train.detach().numpy())
y_test_pred = scaler.inverse_transform(y_test_pred.detach().numpy())
y_test = scaler.inverse_transform(y_test.detach().numpy())
# Calculate root mean squared error
trainScore = math.sqrt(mean_squared_error(y_train[:,0], y_train_pred[:,0]))
print('Train Score: %.2f RMSE' % (trainScore))
testScore = math.sqrt(mean_squared_error(y_test[:,0], y_test_pred[:,0]))
print('Test Score: %.2f RMSE' % (testScore))
Eventually, we’ll carry out inferencing and save the output signals dataframe for backtesting:
# Inferencing
y_inf_pred, y_inf = predict_price(df_test, model, scaler)
signal = gen_signal(y_inf_pred, y_inf)
# Save signals as csv file
output_df = pd.DataFrame(index=df_test.index)
output_df['signal'] = signal
output_df.index.name = "Date"
output_filename = 'output/' + symbol + '_output.csv'
output_df.to_csv(output_filename)
With the signals csv files, we could simply run
output-backtester_wrapper.py
in the
same directory that would load all the files in the output directory and run it with the backtester_wrapper
to compute the evaluation metrics.Multi-feature LSTM model¶
The implememtation of the single-feature LSTM model could be found in
integrated-strategy/LSTM-train_wrapper.py
.We’ll focus on looking at the
LSTM_predict
function, as the main
function is simply a wrapper that
calls the LSTM_predict
function with different ticker symbols.The code structure of the multi-feature LSTM and single-feature LSTM is largely the name, except that we’ll
need the merged dataframe as the input and we’ll need to change the input/output dimensions of the model.
# load file
dir_name = os.getcwd()
data_dir = os.path.join(dir_name,"database_real/machine_learning_data/")
sentiment_data_dir=os.path.join(dir_name,"database/sentiment_data/data-result/")
# Get merged df with stock tick and sentiment scores
df, scaled, scaler = merge_data(symbol, data_dir, sentiment_data_dir, strategy)
look_back = 60 # choose sequence length
x_train, y_train, x_test_df, y_test_df = load_data(scaled, look_back)
# make training and test sets in torch
x_train = torch.from_numpy(x_train).type(torch.Tensor)
x_test = torch.from_numpy(x_test_df).type(torch.Tensor)
y_train = torch.from_numpy(y_train).type(torch.Tensor)
y_test = torch.from_numpy(y_test_df).type(torch.Tensor)
# Hyperparameters
num_epochs = 100
lr = 0.01
batch_size = 72
input_dim = 7
hidden_dim = 64
num_layers = 4
output_dim = 7
torch.manual_seed(1) # set seed
print("Hyperparameters:")
print("input_dim: ", input_dim, ", hidden_dim: ", hidden_dim, ", num_layers: ", num_layers, ", output_dim", output_dim)
print("num_epochs: ", num_epochs, ", batch_size: ", batch_size, ", lr: ", lr)
train = torch.utils.data.TensorDataset(x_train,y_train)
test = torch.utils.data.TensorDataset(x_test,y_test)
train_loader = torch.utils.data.DataLoader(dataset=train,
batch_size=batch_size,
shuffle=False)
test_loader = torch.utils.data.DataLoader(dataset=test,
batch_size=batch_size,
shuffle=False)
model = LSTM(input_dim=input_dim, hidden_dim=hidden_dim, output_dim=output_dim, num_layers=num_layers)
loss_fn = torch.nn.MSELoss()
optimiser = torch.optim.Adam(model.parameters(), lr=lr)
hist = np.zeros(num_epochs)
# Number of steps to unroll
seq_dim = look_back - 1
# Train model
for t in range(num_epochs):
for i, (train_data, train_label) in enumerate(train_loader):
# Forward pass
train_pred = model(train_data)
loss = loss_fn(train_pred, train_label)
hist[t] = loss.item()
# Zero out gradient, else they will accumulate between epochs
optimiser.zero_grad()
# Backward pass
loss.backward()
# Update parameters
optimiser.step()
if t % 10 == 0 and t != 0:
y_train_pred = model(x_train)
loss = loss_fn(y_train_pred, y_train)
print("Epoch ", t, "MSE: ", loss.item())
# Make predictions
y_test_pred = model(x_test)
# Invert predictions
y_train_pred = scaler.inverse_transform(y_train_pred.detach().numpy())
y_train = scaler.inverse_transform(y_train.detach().numpy())
y_test_pred = scaler.inverse_transform(y_test_pred.detach().numpy())
y_test = scaler.inverse_transform(y_test.detach().numpy())
# Calculate root mean squared error
trainScore = math.sqrt(mean_squared_error(y_train[:,0], y_train_pred[:,0]))
print('Train Score: %.2f RMSE' % (trainScore))
testScore = math.sqrt(mean_squared_error(y_test[:,0], y_test_pred[:,0]))
print('Test Score: %.2f RMSE' % (testScore))
visualise(df, y_test[:,0], y_test_pred[:,0], pred_filename)
signal_dataframe = gen_signal(y_test_pred[:,0], y_test[:,0], df[len(df)-len(y_test):].index, by_trend=True)
# Save signals as csv file
output_filename = 'LSTM_output_trend/' + symbol + '_output.csv'
signal_dataframe.to_csv(output_filename,index=False)
Note that we’ll need to pass the ticker symbol name and the name of the technical indicator (to be included in
the merged dataframe) to the
LSTM_predict
function, for example by calling in this way:LSTM_predict('0001', 'macd-crossover')
Similarly, we could run the
output-backtester_wrapper.py
file to backtest the output signals.References
Image sources
- 1
By Christopher Olah - Understanding LSTM Networks, https://colah.github.io/posts/2015-08-Understanding-LSTMs
Attention
All investments entail inherent risk. This repository seeks to solely educate
people on methodologies to build and evaluate algorithmic trading strategies.
All final investment decisions are yours and as a result you could make or lose money.


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Pain gets the better of Oosthuizen as he hangs tough at Sun City
by Lynn Butler - Sport24Sun City - Louis Oosthuizen said fatigue got the better of him in the second round at the Nedbank Golf Challenge on Friday.
The 2010 Open champion spent Wednesday at a hospital in Rustenburg treating a kidney stone, which almost resulted in him not teeing off in the opening round on Thursday.
However, Oosthuizen bounced back to take the overnight lead by three shots heading into Round 2.
On Friday, the Gary Player Country Club was not his friend this time around as Oosthuizen posted a level par round of 72 to remain on 9-under - two shots behind leader and fellow countryman Zander Lombard.
"I was fine starting, the last six holes got me a bit. The tee shot at the 14th, I felt pain in the right lower side and the next two holes, I was thinking about that and I didn't really focus on what I had to do," Oosthuizen told reporters on Friday.
"I could feel that I wasn't on song with the way I was hitting it. It's fatigue, I need to rest well for tomorrow."
Only one golfer managed to hit a bogey-free second round - Malaysian's Gavin Green - who shot a 2-under 70.
Oosthuizen said it was tough day and was bound to get tougher heading into the weekend.
"It was tough... it was guessing the whole day so it was difficult around this golf course. Other than Zander's round, I don't think there's low rounds out there," said SA's top ranked golfer.
"Hopefully this was my bad one for the week but I think the golf course is going to play really tough on the weekend.
"You could see the sort of pins and I think on the weekend we're going to have a proper test."
Oosthuizen will tee off alongside Belgian Thomas Detry and leader Zander Lombard at 10:20 on Saturday.
Leading scores after the second round of the European Tour's Nedbank Golf Challenge on Friday at the par-72 Gary Player Country Club in Sun City (RSA unless noted):
133 - Zander Lombard 68-65
135 - Louis Oosthuizen 63-72
137 - Thomas Detry (BEL) 66-71
138 - Tommy Fleetwood (ENG) 69-69, Marcus Kinhult (SWE) 69-69
139 - Joachim B Hansen (DEN) 69-70, Aaron Rai (ENG) 70-69, Jason Scrivener (AUS) 69-70
140 - Nacho Elvira (ESP) 70-70, Matthew Fitzpatrick (ENG) 71-69, Mikko Korhonen (FIN) 70-70, Guido Migliozzi (ITA) 67-73, Alvaro Quiros (ESP) 70-70, Paul Waring (ENG) 69-71, Bernd Wiesberger (AUT) 71-69


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